Big knowledge visualization of the COVID-19 CIDO ontology evolution

Area of Science:

• Ontology development and knowledge representation
• Bioinformatics and computational biology
• Information visualization

Background:

• The COVID-19 pandemic necessitates standardized ontologies for research.
• The Coronavirus Infectious Disease Ontology (CIDO) is a large, frequently updated resource.
• Researchers need efficient methods to track CIDO's content evolution and relevance.

Purpose of the Study:

• To develop a framework for visualizing content changes between ontology releases.
• To provide researchers with a "big picture" overview of ontology evolution.
• To enhance the usability of large, dynamic knowledge bases.

Main Methods:

• Introduced the Diff Weighted Aggregate Taxonomy (DWAT) framework.
• Utilized a layered approach to visualize ontology differences.
• Applied the DWAT framework to the Coronavirus Infectious Disease Ontology (CIDO).

Main Results:

• Demonstrated the visualization of CIDO's evolution between 2020 and 2022.
• Showcased a layered approach, allowing drill-down into major and secondary subjects.
• Illustrated how DWAT provides insights into ontology content changes.

Conclusions:

• The Diff Weighted Aggregate Taxonomy (DWAT) enables a layered visualization of ontology content changes.
• DWAT facilitates a comprehensive understanding of knowledge base evolution.
• This approach aids researchers in assessing the relevance of new ontology concepts.